import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_SCREAMING_SNAKE_CASE 		=      []
for i in range(6):
     # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
     rename_keys.append(
         (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
     )
     rename_keys.append(
         (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
     )
     rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
     rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
     rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
     rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
     rename_keys.append(
         (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
     )
     rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
     rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
     rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
     # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
     )
     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
     )
     rename_keys.append(
         (
             F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
             F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
         )
     )
     rename_keys.append(
         (
             F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
             F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
         )
     )
     rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
     rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
     rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
     rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
     )
     rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
     )
     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
     )
     rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
     rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

     # q, k, v projections in self/cross-attention in decoder for conditional DETR
     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
     )
     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
     )
     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
     )
     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
     )
     rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
     )
     # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
     )
     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
     )
     rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
     )

     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
     )
     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
     )
     rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
     rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
     rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
     )
     # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
     )
     rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
     rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
     rename_keys.append(
         (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
     )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Union[str, Any]:







 '''simple docstring'''
 UpperCamelCase			  =      state_dict.pop(UpperCamelCase_       )
 UpperCamelCase			  =      val





def 			lowercase(	UpperCamelCase_       )		->	Dict:







 '''simple docstring'''
 UpperCamelCase			  =      OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   UpperCamelCase			  =      key.replace("""backbone.0.body"""							,					"""backbone.conv_encoder.model"""       )
   UpperCamelCase			  =      value
  else:
   UpperCamelCase			  =      value

 return new_state_dict





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_=False       )		->	Optional[int]:







 '''simple docstring'''
 UpperCamelCase			  =      """"""
 if is_panoptic:
  UpperCamelCase			  =      """conditional_detr."""

 # first: transformer encoder
 for i in range(6       ):
  # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
  UpperCamelCase			  =      state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
  UpperCamelCase			  =      state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""       )
  # next, add query, keys and values (in that order) to the state dict
  UpperCamelCase			  =      in_proj_weight[:256, :]
  UpperCamelCase			  =      in_proj_bias[:256]
  UpperCamelCase			  =      in_proj_weight[256:512, :]
  UpperCamelCase			  =      in_proj_bias[256:512]
  UpperCamelCase			  =      in_proj_weight[-256:, :]
  UpperCamelCase			  =      in_proj_bias[-256:]





def 			lowercase(	)		->	Optional[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCamelCase			  =      Image.open(requests.get(UpperCamelCase_							,					stream=UpperCamelCase_       ).raw       )

 return im





@torch.no_grad()
def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	List[str]:







 '''simple docstring'''
 UpperCamelCase			  =      ConditionalDetrConfig()
 # set backbone and dilation attributes
 if "resnet101" in model_name:
  UpperCamelCase			  =      """resnet101"""
 if "dc5" in model_name:
  UpperCamelCase			  =      True
 UpperCamelCase			  =      """panoptic""" in model_name
 if is_panoptic:
  UpperCamelCase			  =      250
 else:
  UpperCamelCase			  =      91
  UpperCamelCase			  =      """huggingface/label-files"""
  UpperCamelCase			  =      """coco-detection-id2label.json"""
  UpperCamelCase			  =      json.load(open(hf_hub_download(UpperCamelCase_							,					UpperCamelCase_							,					repo_type="""dataset"""       )							,					"""r"""       )       )
  UpperCamelCase			  =      {int(UpperCamelCase_       ): v for k, v in idalabel.items()}
  UpperCamelCase			  =      idalabel
  UpperCamelCase			  =      {v: k for k, v in idalabel.items()}

 # load image processor
 UpperCamelCase			  =      """coco_panoptic""" if is_panoptic else """coco_detection"""
 UpperCamelCase			  =      ConditionalDetrImageProcessor(format=UpperCamelCase_       )

 # prepare image
 UpperCamelCase			  =      prepare_img()
 UpperCamelCase			  =      image_processor(images=UpperCamelCase_							,					return_tensors="""pt"""       )
 UpperCamelCase			  =      encoding["""pixel_values"""]

 logger.info(f"""Converting model {model_name}..."""       )

 # load original model from torch hub
 UpperCamelCase			  =      torch.hub.load("""DeppMeng/ConditionalDETR"""							,					UpperCamelCase_							,					pretrained=UpperCamelCase_       ).eval()
 UpperCamelCase			  =      conditional_detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   UpperCamelCase			  =      """conditional_detr.""" + src
  rename_key(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 UpperCamelCase			  =      rename_backbone_keys(UpperCamelCase_       )
 # query, key and value matrices need special treatment
 read_in_q_k_v(UpperCamelCase_							,					is_panoptic=UpperCamelCase_       )
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 UpperCamelCase			  =      """conditional_detr.model.""" if is_panoptic else """model."""
 for key in state_dict.copy().keys():
  if is_panoptic:
   if (
       key.startswith("""conditional_detr"""       )
       and not key.startswith("""class_labels_classifier"""       )
       and not key.startswith("""bbox_predictor"""       )
   ):
    UpperCamelCase			  =      state_dict.pop(UpperCamelCase_       )
    UpperCamelCase			  =      val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    UpperCamelCase			  =      state_dict.pop(UpperCamelCase_       )
    UpperCamelCase			  =      val
   elif key.startswith("""bbox_attention"""       ) or key.startswith("""mask_head"""       ):
    continue
   else:
    UpperCamelCase			  =      state_dict.pop(UpperCamelCase_       )
    UpperCamelCase			  =      val
  else:
   if not key.startswith("""class_labels_classifier"""       ) and not key.startswith("""bbox_predictor"""       ):
    UpperCamelCase			  =      state_dict.pop(UpperCamelCase_       )
    UpperCamelCase			  =      val
    # finally, create HuggingFace model and load state dict
 UpperCamelCase			  =      ConditionalDetrForSegmentation(UpperCamelCase_       ) if is_panoptic else ConditionalDetrForObjectDetection(UpperCamelCase_       )
 model.load_state_dict(UpperCamelCase_       )
 model.eval()
 model.push_to_hub(repo_id=UpperCamelCase_							,					organization="""DepuMeng"""							,					commit_message="""Add model"""       )
 # verify our conversion
 UpperCamelCase			  =      conditional_detr(UpperCamelCase_       )
 UpperCamelCase			  =      model(UpperCamelCase_       )
 assert torch.allclose(outputs.logits							,					original_outputs["""pred_logits"""]							,					atol=1E-4       )
 assert torch.allclose(outputs.pred_boxes							,					original_outputs["""pred_boxes"""]							,					atol=1E-4       )
 if is_panoptic:
  assert torch.allclose(outputs.pred_masks							,					original_outputs["""pred_masks"""]							,					atol=1E-4       )

 # Save model and image processor
 logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""       )
 Path(UpperCamelCase_       ).mkdir(exist_ok=UpperCamelCase_       )
 model.save_pretrained(UpperCamelCase_       )
 image_processor.save_pretrained(UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()

     parser.add_argument(
         """--model_name""",
         default="""conditional_detr_resnet50""",
         type=str,
         help="""Name of the CONDITIONAL_DETR model you'd like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

from __future__ import annotations
class SCREAMING_SNAKE_CASE_		:







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      text, pattern
    UpperCamelCase  ,     UpperCamelCase			  =      len(lowerCamelCase_				), len(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    for i in range(self.patLen - 1   ,   -1   ,   -1				):
     if char == self.pattern[i]:
      return i
    return -1







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						int				):





    """simple docstring"""


    for i in range(self.patLen - 1   ,   -1   ,   -1				):
     if self.pattern[i] != self.text[current_pos + i]:
      return current_pos + i
    return -1







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      []
    for i in range(self.textLen - self.patLen + 1				):
     UpperCamelCase			  =      self.mismatch_in_text(lowerCamelCase_				)
     if mismatch_index == -1:
      positions.append(lowerCamelCase_				)
     else:
      UpperCamelCase			  =      self.match_in_pattern(self.text[mismatch_index]				)
      UpperCamelCase			  =      (
          mismatch_index - match_index
      )  # shifting index lgtm [py/multiple-definition]
    return positions





_SCREAMING_SNAKE_CASE 		=      """ABAABA"""
_SCREAMING_SNAKE_CASE 		=      """AB"""
_SCREAMING_SNAKE_CASE 		=      BoyerMooreSearch(text, pattern)
_SCREAMING_SNAKE_CASE 		=      bms.bad_character_heuristic()

if len(positions) == 0:
     print("""No match found""")
else:
     print("""Pattern found in following positions: """)
     print(positions)
